


'''simple docstring'''







import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase     :  Optional[int]											=    logging.get_logger(__name__)

lowerCAmelCase     :  List[Any]											=    '''▁'''

lowerCAmelCase     :  List[str]											=    {'''vocab_file''': '''prophetnet.tokenizer'''}

lowerCAmelCase     :  Optional[Any]											=    {
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

lowerCAmelCase     :  Optional[Any]											=    {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

lowerCAmelCase     :  Optional[Any]											=    {
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							collections.OrderedDict()
       with open(_A ,    'r' ,    encoding='utf-8'       ) as reader:
              _lowerCAmelCase     :				Union[str, Any]       =							reader.readlines()
       for index, token in enumerate(_A       ):
              _lowerCAmelCase     :				List[str]       =							token.rstrip('\n'       )
              _lowerCAmelCase     :				Dict       =							index
       return vocab
class 						UpperCamelCase__       (			_UpperCAmelCase						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ['input_ids', 'attention_mask']







     def __init__(			self   ,						snake_case__   ,						snake_case__="[SEP]"   ,						snake_case__="[SEP]"   ,						snake_case__="[SEP]"   ,						snake_case__="[UNK]"   ,						snake_case__="[PAD]"   ,						snake_case__="[CLS]"   ,						snake_case__="[MASK]"   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=__UpperCamelCase   ,						eos_token=__UpperCamelCase   ,						sep_token=__UpperCamelCase   ,						unk_token=__UpperCamelCase   ,						pad_token=__UpperCamelCase   ,						cls_token=__UpperCamelCase   ,						mask_token=__UpperCamelCase   ,						sp_model_kwargs=self.sp_model_kwargs   ,						**__UpperCamelCase   ,						)

            try:
                   import sentencepiece as spm
            except ImportError:
                   logger.warning(
                       'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                       ' pip install sentencepiece' )
                   raise

            _lowerCAmelCase     :				str       =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(str(__UpperCamelCase ) )
            _lowerCAmelCase     :				str       =							vocab_file

            # Original fairseq vocab and spm vocab must be "aligned":
            # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
            # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
            # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
            # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

            # put special tokens and [unused] tokens into the vocab
            _lowerCAmelCase     :				Optional[int]       =							{'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

            for i in range(10 ):
                   _lowerCAmelCase     :				str       =							F'[unused{i}]'
                   _lowerCAmelCase     :				str       =							5 + i

            # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
            _lowerCAmelCase     :				Tuple       =							12
            _lowerCAmelCase     :				Optional[int]       =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
            for k in self.fairseq_tokens_to_ids.keys():
                   self.unique_no_split_tokens.append(__UpperCamelCase )







     def __getstate__(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.__dict__.copy()
            _lowerCAmelCase     :				Dict       =							None
            return state







     def __setstate__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							d
            try:
                   import sentencepiece as spm
            except ImportError:
                   logger.warning(
                       'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                       ' pip install sentencepiece' )
                   raise

            # for backward compatibility
            if not hasattr(self   ,						'sp_model_kwargs' ):
                   _lowerCAmelCase     :				Dict       =							{}

            _lowerCAmelCase     :				Union[str, Any]       =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(self.vocab_file )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = False ):

            '''simple docstring'''

            if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=__UpperCamelCase   ,						token_ids_a=__UpperCamelCase   ,						already_has_special_tokens=__UpperCamelCase )

            if token_ids_a is None:
                   return ([0] * len(__UpperCamelCase )) + [1]
            return ([0] * len(__UpperCamelCase )) + [1] + ([0] * len(__UpperCamelCase )) + [1]







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							[self.sep_token_id]

            if token_ids_a is None:
                   return len(token_ids_a + sep ) * [0]
            return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]







     @property
     def a						(			self ):

            '''simple docstring'''

            return len(self.sp_model ) + self.fairseq_offset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							{self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            return self.sp_model.encode(__UpperCamelCase   ,						out_type=__UpperCamelCase )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
            _lowerCAmelCase     :				Union[str, Any]       =							self.sp_model.PieceToId(__UpperCamelCase )

            # Need to return unknown token if the SP model returned 0
            return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if index in self.fairseq_ids_to_tokens:
                   return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(index - self.fairseq_offset )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							''.join(__UpperCamelCase ).replace(__UpperCamelCase   ,						' ' ).strip()
            return out_string







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            if not os.path.isdir(__UpperCamelCase ):
                   logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
                   return
            _lowerCAmelCase     :				List[Any]       =							os.path.join(
                __UpperCamelCase   ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

            if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(self.vocab_file ):
                   copyfile(self.vocab_file   ,						__UpperCamelCase )
            elif not os.path.isfile(self.vocab_file ):
                   with open(__UpperCamelCase   ,						'wb' ) as fi:
                          _lowerCAmelCase     :				Dict       =							self.sp_model.serialized_model_proto()
                          fi.write(__UpperCamelCase )

            return (out_vocab_file,)







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            if token_ids_a is None:
                   return token_ids_a + [self.sep_token_id]
            _lowerCAmelCase     :				Union[str, Any]       =							[self.sep_token_id]
            return token_ids_a + sep + token_ids_a + sep



"""simple docstring"""



import math
import unittest


def 			_UpperCamelCase						(    _A   )     ->	bool:





    """simple docstring"""

    assert isinstance(_A       ,   _A   ) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5       ,   int(math.sqrt(_A   ) + 1   )       ,   6   ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True




class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							int  )		->Optional[int]:






        '''simple docstring'''






        self.assertTrue(is_prime(2  )  )
        self.assertTrue(is_prime(3  )  )
        self.assertTrue(is_prime(5  )  )
        self.assertTrue(is_prime(7  )  )
        self.assertTrue(is_prime(11  )  )
        self.assertTrue(is_prime(13  )  )
        self.assertTrue(is_prime(17  )  )
        self.assertTrue(is_prime(19  )  )
        self.assertTrue(is_prime(23  )  )
        self.assertTrue(is_prime(29  )  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            is_prime(-19  )
        self.assertFalse(
            is_prime(0  )  ,       """Zero doesn't have any positive factors, primes must have exactly two."""  ,       )
        self.assertFalse(
            is_prime(1  )  ,       """One only has 1 positive factor, primes must have exactly two."""  ,       )
        self.assertFalse(is_prime(2 * 2  )  )
        self.assertFalse(is_prime(2 * 3  )  )
        self.assertFalse(is_prime(3 * 3  )  )
        self.assertFalse(is_prime(3 * 5  )  )
        self.assertFalse(is_prime(3 * 5 * 7  )  )


if __name__ == "__main__":
 unittest.main()